Abstract
In this paper, a Hessian-based strategy, based on the central medialness adaptive principle, was adapted and proposed in a multiscale approach for the 3D segmentation of pulmonary nodules in chest CT scans. This proposal is compared with another well stated Hessian based strategy of the literature, for nodule extraction, in order to demonstrate its accuracy.
Several scans from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database were employed in the test and validation procedure. The scans include a large and heterogeneous set of 569 solid and mostly solid nodules with a large variability in the nodule characteristics and image conditions. The results demonstrated that the proposal offers correct results, similar to the performance of the radiologists, providing accurate nodule segmentations that perform the desirable scenario for a posterior analysis and the eventual lung cancer diagnosis.
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Acknowledgments
This work is financed by project NORTE-01-0145-FEDER-000016 by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF); and through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within the grant contract SFRH/BPD/85663/2012 (J. Novo).
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Gonçalves, L., Novo, J., Campilho, A. (2016). Central Medialness Adaptive Strategy for 3D Lung Nodule Segmentation in Thoracic CT Images. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_65
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DOI: https://doi.org/10.1007/978-3-319-41501-7_65
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